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Segmentation partitions an image into its constituent parts. It isessentially the pre-processing stage of image analysis and computer vision. Inthis work, T1 and T2 weighted brain magnetic resonance images are segmentedusing multilevel thresholding and bacterial foraging optimization (BFO)algorithm. The thresholds are obtained by maximizing the between class variance(multilevel Otsu method) of the image. The BFO algorithm is used to optimizethe threshold searching process. The edges are then obtained from thethresholded image by comparing the intensity of each pixel with its eightconnected neighbourhood. Post processing is performed to remove spuriousresponses in the segmented image. The proposed segmentation technique isevaluated using edge detector evaluation parameters such as figure of merit,Rand Index and variation of information. The proposed brain MR imagesegmentation technique outperforms the traditional edge detectors such as cannyand sobel.

Bacterial foraging optimization based brain magnetic resonance image segmentation